Great Bear AI

Robust AI for
high-stakes decisions.

In defense, markets, and sport, the cost of being wrong is nonlinear. We build systems that keep many plausible futures in view, weigh the risk in the tail, and point to the decision worth making — with a human in command.

The Great Bear points to the North Star. In a noisy sky, we point you to where to go.

Multi-hypothesis forecastingTail-risk optimizationHuman-in-command
The thesis

One prediction is not enough.

The real world is noisy, adversarial, and unforgiving of overconfidence. A single forecast hides the futures that would hurt you most. We keep a small set of plausible futures alive, measure the risk in the tail, and recommend the action that holds up across the futures that matter — so you can hedge instead of betting on one guess.

― one guess▬ plausible futures● robust pick
01

See many futures

A structured set of plausible outcomes — each individually credible, collectively diverse.

02

Weigh the tail

Optimize against the worst credible cases, not just the average. Tail-risk is the point.

03

Point to a decision

A clear, auditable recommendation — with a human in command of every call.

Products

Three products, one core.

Each keeps many futures in view, then points to a decision — the same engine, applied where the cost of being wrong is highest.

Flagship

Drodee

Counter-swarm defense · Flagship

Real-time decision support for counter-swarm defense.

Fuse sensors. Forecast swarm futures. Allocate scarce defenders. Keep humans in command. Drodee turns disconnected radars, RF detectors, cameras, and interceptors into one real-time picture, then recommends robust, human-approved allocations under breach tail-risk — "Cloudflare for defended airspace."

  • Software-only, non-kinetic decision support
  • Recommends and prioritizes — a human approves every action
  • Robust allocation when sensors disagree and the swarm saturates defenders

Demo uses synthetic data — a decision-support research preview, not an operational benchmark.

Pre-launch · World Cup 2026

Sports Intelligence

Football scenario intelligence

Not one prediction — a calibrated set of plausible match futures.

Sports Intelligence turns a fixture into a structured ensemble of plausible match scenarios, calibrated against the market, with the what-ifs that actually change a decision. Built for analysts, desks, and the genuinely curious.

  • A spread of credible match futures, not a single scoreline
  • Calibrated probabilities and what-if scenarios
  • Scenario language throughout — plausible futures and probabilities
Live · qelly.ai

Qelly

AI markets intelligence · In collaboration with Juan Mendoza, CFA

Find the edge across markets, in real time.

Qelly watches dozens of markets at once, detects where price and probability disagree, sizes each opportunity by the mathematics of edge and risk, and lets any AI agent act on it. A Great Bear AI collaboration with Juan Mendoza, CFA — live today.

  • Real-time edge detection across dozens of books
  • Mathematically-sized positions with disciplined risk control
  • An API and agent tools so any system can act on the signal
Research platform

The engine: structured hypothesis hedging.

Our systems learn a structured, discrete prior over plausible futures — a handful of hypotheses that are each individually credible and collectively diverse. A shared decoder turns them into concrete futures; a tail-risk layer turns those futures into a robust decision. The result is calibrated multi-hypothesis reasoning that hedges instead of betting on a single guess. Classical by default.

We describe our methods in peer-reviewed work; details follow as that work is published.

01Context

Noisy, partial observations

02Structured prior

A discrete distribution over plausible modes

03K hypotheses

Correlated and diverse — neither collapsed nor random

04Shared decoder

K concrete, plausible futures

05Tail-risk layer

A robust, CVaR-aware decision

Quantum Lab

Quantum-assisted optimization, used honestly.

Many of our decisions reduce to hard discrete optimization. We formulate them as QUBO / Ising problems and evaluate quantum annealing as one backend in a hybrid solver portfolio — alongside greedy, classical, and simulated-annealing solvers. Classical solvers are the default and the fallback. We benchmark against the strongest classical baseline, and we make no quantum-advantage claim.

  • Classical by default — a classical fallback is always available
  • No quantum-advantage claim, anywhere
  • Benchmarked against the strongest classical baseline

Solver portfolio

  • GreedyFast heuristic baseline
  • ClassicalExact / convex where it fits
  • Simulated annealingRobust discrete search
  • Quantum annealerOne backend, evaluated honestly

Classical solvers run by default; a classical fallback is always available.

About

A research-led company for decisions that matter.

Great Bear AI LLC builds decision systems for environments where being wrong is expensive — where sensors are noisy, adversaries adapt, and loss is nonlinear. We pair structured generative models with robust optimization and quantum-assisted discrete search, and we hold ourselves to honest benchmarks.

Honest by default

Every comparative claim traces to a measured result. No hype, no quantum magic.

Human-in-command

Our systems recommend and prioritize. People decide.

One core, many domains

Multi-hypothesis forecasting and tail-risk decisions — from defense to markets to sport.

Luis Lozano
Founder

A physicist working at the intersection of generative AI, robust optimization, financial engineering, and quantum-inspired methods. Great Bear AI is the umbrella over a small portfolio of products that share one mathematical core: keep many futures in view, then decide.

Contact

Find your North Star.

Each product has its own access on its own site — explore Drodee, Sports Intelligence, and Qelly above. For pilots, partnerships, or press, reach Great Bear AI directly.